Categorical variables were compared using the chi-squared test. Non-normally distributed data were analysed using the Mann–Whitney U test. Comparisons were made for the main explanatory variable, namely the extent of downstaging (that is upstaged, no change, or downstaged by one stage, two stages, or three or more stages). survival was estimated using Kaplan–Meier survival curves and compared using the log rank test. Multivariable analyses used Cox proportional hazards models to adjust for clinically relevant variables to produce adjusted HR and 95 per cent confidence intervals. P < 0.050 was considered to be statistically significant. Data analysis was performed using R Foundation Statistical Software (R 3.2.2) with the TableOne, ggplot2, Hmisc, Matchit, and survival packages (R Foundation for Statistical Computing, Vienna, Austria) as previously described10 ,11 (link). This study was exempt from Institutional Review Board approval.
Matchit
Manufactured by R Project for Statistical Computing
Sourced in Austria
Matchit is a package for R that provides methods for causal inference using matching techniques. It allows users to perform propensity score matching, coarsened exact matching, and other matching methods to estimate causal effects from observational data.
Automatically generated - may contain errors
Lab products found in correlation
Ggplot2, by R Project for Statistical Computing (6 mentions)
Hmisc, by R Project for Statistical Computing (6 mentions)
Tableone, by R Project for Statistical Computing (6 mentions)
Survival, by R Project for Statistical Computing (4 mentions)
R version 3, by R Project for Statistical Computing (2 mentions)
R foundation statistical software r 3.2.2, by R Project for Statistical Computing (1 mentions)
Prism version 7, by GraphPad (1 mentions)
Jmp software v 15, by SAS Institute (1 mentions)
10 protocols using matchit
1
Survival Analysis of Downstaging in Cancer
2
Organ Chamber Experiments Analysis
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For data and analysis from the organ chamber experiments, contractions are expressed as the percentages to the response to 60 mM KCl obtained at the beginning of the experiments. Relaxations were calculated as the percentages to the phenylephrine-induced contractions. The results are shown as mean ± S.e.m. or mean ± Sd with n referring to the number of animals used. The responses were also analysed and compared with the area under the concentration–response curve (AUC). Concentration–response curve were plotted with Prism version 7 (GraphPad Software, San Diego, CA, USA). Statistical analyses were performed by using Student’s unpaired t-test, with or without Welch’s correction, as appropriate, for two group comparisons. To compare values of three groups, a one-way analysis of variance (ANOVA) was used, followed by Tukey post hoc test. The intensity of western blot was calculated using the computerized program (ImageJ software, National Institutes of Health). Optimal full matching in the human cohort was achieved using the R package ‘matchit’ (version 4.4.0; R Foundation, Vienna, Austria). Covariate balance was assessed visually and by interrogating the absolute standardized mean difference of included covariates. P values less than 0.05 were considered to indicate statistically significant differences.
3
Analyzing Survival in Cancer Patients
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Categorical variables were compared using the Chi squared test. Non-normally distributed data were analyzed using the Mann–Whitney U test. survival was estimated using Kaplan–Meier survival curves and compared using the log-rank test. Multivariable analyses used Cox proportional hazards models. Stratified survival analyses by underlying histology (adenocarcinoma and squamous cell carcinoma) and by response to neoadjuvant therapy classification were performed. Analyses were also performed according to degree of downstaging (> 3 stages, 3 stages, 2 stages, and 1 stage). A p value of < 0.05 was considered to be statistically significant. Data analysis was performed using R Foundation Statistical software (R 3.2.2) with TableOne, ggplot2, Hmisc, Matchit, and survival packages (R Foundation for Statistical Computing, Vienna, Austria) as previously described.17
4
Survival Analysis of Surgical Outcomes
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Between‐group differences in patient characteristics were assessed using the Student's t‐test or Fisher's exact test. Kaplan–Meier survival curves were drawn and differences in survival between the two groups were examined using the log‐rank test. OS was defined as the interval between surgery and death from any cause and RFS as the interval between surgery and initial relapse or death from any cause. Recurrence was confirmed radiologically or pathologically.
All statistical analyses were performed using JMP software v. 15 (SAS Institute, Cary, NC, USA). PSM was performed using MatchIt (The R Foundation for Statistical Computing, Vienna, Austria). All P‐values were two‐sided and considered statistically significant at P < .05.
All statistical analyses were performed using JMP software v. 15 (SAS Institute, Cary, NC, USA). PSM was performed using MatchIt (The R Foundation for Statistical Computing, Vienna, Austria). All P‐values were two‐sided and considered statistically significant at P < .05.
5
Survival Analysis of Disease Outcomes
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Categorical variables were compared using the Chi square test, and non-normally distributed data were analyzed using the Mann–Whitney U test. survival was estimated using Kaplan–Meier survival curves and compared using the log-rank test. Multivariable analyses used Cox proportional hazards models (“Appendix 1 ”). A comparison of outcomes between 5-year periods (1989–1993, 1994–1998, 1999–2003, 2004–2008, 2009–2013, 2014–2018) was also performed. For the final cohort, patients were included up to January 2017 to allow for a minimum 3 years of follow-up. A p value < 0.05 was considered to be statistically significant. Data analysis was performed using R Foundation Statistical software (R 3.2.2) with TableOne, ggplot2, Hmisc, Matchit and survival packages (The R Foundation for Statistical Computing, Vienna, Austria), as previously reported.
As a review of past practice and outcomes, this study was deemed exempt from the need for ethical approval.
As a review of past practice and outcomes, this study was deemed exempt from the need for ethical approval.
6
Consensus Analysis of Research Protocols
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We used Cronbach’s α to evaluate consensus quantitatively among the international expert panel; a Cronbach’s α value of at least 0.80 was representative of an acceptable measure of internal reliability.20 (link)–23 (link) Categorical variables were compared using the χ2 test. Non-normally distributed data were analyzed using the Mann-Whitney U test for comparisons across 2 groups, and the Kruskal-Wallis test for comparisons of more than 2 groups. Stratified analyses were performed for responses from the second voting round by: annual department volume (≤50, 51–100, ≥101 procedures) and annual surgeon volume (≤20, 21–50, ≥51 procedures). A P value of <0.05 was considered statistically significant and no adjustments were made for multiple comparisons. Heat maps were developed to display the level of consensus (ie, green: ≥80% agreement, yellow: 70%–80%, and red: <70% agreement) across the different research questions.24 (link) Data analysis was performed using R version 3.2.2, with TableOne, ggplot2, Hmisc, Matchit, and survival packages (R Foundation for Statistical Computing, Vienna, Austria).
7
Disease Risk Score Matching for ESRD
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Disease risk score generation and matching were performed using the R package MatchIt (R Foundation for Statistical Computing, Vienna, Austria). Briefly, disease risk score estimates, which represent the probability of progression to ESRD for enrolled patients, were generated using a logistic regression model derived from clinical variables. Following disease risk score generation, patients were matched by using a 1: 1 optimal matching method. Matching was performed without replacement, and non-matched results were discarded. Accordingly, the patients diagnosed with ICD-code N18.1 were included at first. However, after disease scoring matching, no patients diagnosed with ICD-code N18.1 in the non-ESRD group were matched with patients diagnosed with ICD-code N18.1 in the ESRD group.
8
Propensity Score Matched Survival Analysis
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Categorical variables were compared using the Chi-square test, and non-normally distributed data were analyzed using the Mann–Whitney U test. Survival was estimated using Kaplan–Meier survival curves and compared using the log-rank test. Multivariable analyses used Cox proportional hazards models. The conditional probability of receiving AC, i.e. the propensity score, was estimated using a multivariable logistic regression model including all variables listed above. We then created balanced cohorts using 1-to-1 nearest-neighbor PSM without replacement (caliper width 0.1 standard deviations).23 (link) Balance diagnostics were conducted using standardized mean differences, with a value < 0.1 indicating good balance.23 (link) Sensitivity and interaction analyses were performed by nodal status (i.e. N0, N1, N2/3), margin status (i.e. R0, R1), and receipt of NART on long-term survival. A p-value of < 0.05 was considered statistically significant. Data analysis was performed using R Foundation statistical software (R 3.2.2) with TableOne, ggplot2, Hmisc, Matchit, and survival packages (The R Foundation for Statistical Computing, Vienna, Austria) as previously described.24 (link),25 (link) This study was exempt from Institutional Review Board approval.
9
Survival Analysis of Esophageal Cancer
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Categorical variables were compared using the χ 2 test. Non-normally distributed data were analysed using the Mann-Whitney U test. survival was estimated by Kaplan-Meier analysis and curves were compared using the log rank test. Cox proportional hazards models were used for multivariable analyses. Patients who received neoadjuvant therapy before oesophagectomy and those who underwent total gastrectomy were included in separate subgroup analyses. P < 0⋅050 was considered statistically significant. Data analysis was performed using R version 3.2.2, with TableOne, ggplot2, Hmisc, Matchit and survival packages (R Foundation for Statistical Computing, Vienna, Austria) as described previously 10, (link)19 (link) .
10
Propensity Score Matching for Pulmonary Hypertension
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